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Artificial intelligence and machine learning for improving glycemic control in diabetes: Best practices, pitfalls, and opportunities
Objective: Artificial intelligence and machine learning are transforming many fields including
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …
[HTML][HTML] Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes
Background Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood
glucose (BG) regulation that might result in short and long-term health complications and …
glucose (BG) regulation that might result in short and long-term health complications and …
A review of personalized blood glucose prediction strategies for T1DM patients
This paper presents a methodological review of models for predicting blood glucose (BG)
concentration, risks and BG events. The surveyed models are classified into three …
concentration, risks and BG events. The surveyed models are classified into three …
A comprehensive review on smart decision support systems for health care
Medical activity requires responsibility not only based on knowledge and clinical skills, but
also in managing a vast amount of information related to patient care. It is through the …
also in managing a vast amount of information related to patient care. It is through the …
Design and development of diabetes management system using machine learning
RA Sowah, AA Bampoe-Addo… - … of telemedicine and …, 2020 - Wiley Online Library
This paper describes the design and implementation of a software system to improve the
management of diabetes using a machine learning approach and to demonstrate and …
management of diabetes using a machine learning approach and to demonstrate and …
Forecasting of glucose levels and hypoglycemic events: head-to-head comparison of linear and nonlinear data-driven algorithms based on continuous glucose …
In type 1 diabetes management, the availability of algorithms capable of accurately
forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could …
forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could …
An ARIMA model with adaptive orders for predicting blood glucose concentrations and hypoglycemia
J Yang, L Li, Y Shi, X **e - IEEE journal of biomedical and …, 2018 - ieeexplore.ieee.org
The continuous glucose monitoring system is an effective tool, which enables the users to
monitor their blood glucose (BG) levels. Based on the continuous glucose monitoring (CGM) …
monitor their blood glucose (BG) levels. Based on the continuous glucose monitoring (CGM) …
[PDF][PDF] Comparison of statistical logistic regression and random forest machine learning techniques in predicting diabetes
Diabetes is one of the global concerns in the healthcare domain and one of the leading
challenges locally in Saudi Arabia. The prevalence of diabetes is anticipated to rise; early …
challenges locally in Saudi Arabia. The prevalence of diabetes is anticipated to rise; early …
Methylglyoxal–an emerging biomarker for diabetes mellitus diagnosis and its detection methods
Diabetes Mellitus (DM) is one among the supreme metabolic issues observed in history
since 3000 BCE and has gained much interest recently due to the increasing number of …
since 3000 BCE and has gained much interest recently due to the increasing number of …
Breath-based biosensors and system development for noninvasive detection of diabetes: a review
Background and aims In recent years, noninvasive techniques are becoming conspicuous
for diabetes detection. Sweat, tear, saliva, urine and breath-based methods showing …
for diabetes detection. Sweat, tear, saliva, urine and breath-based methods showing …